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*The author of this computation has been verified*
R Software Module: /rwasp_decompose.wasp (opens new window with default values)
Title produced by software: Classical Decomposition
Date of computation: Thu, 09 Dec 2010 19:43:22 +0000
 
Cite this page as follows:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2010/Dec/09/t12919236715k1s6ojk8i1m4x3.htm/, Retrieved Thu, 09 Dec 2010 20:41:16 +0100
 
BibTeX entries for LaTeX users:
@Manual{KEY,
    author = {{YOUR NAME}},
    publisher = {Office for Research Development and Education},
    title = {Statistical Computations at FreeStatistics.org, URL http://www.freestatistics.org/blog/date/2010/Dec/09/t12919236715k1s6ojk8i1m4x3.htm/},
    year = {2010},
}
@Manual{R,
    title = {R: A Language and Environment for Statistical Computing},
    author = {{R Development Core Team}},
    organization = {R Foundation for Statistical Computing},
    address = {Vienna, Austria},
    year = {2010},
    note = {{ISBN} 3-900051-07-0},
    url = {http://www.R-project.org},
}
 
Original text written by user:
 
IsPrivate?
No (this computation is public)
 
User-defined keywords:
 
Dataseries X:
» Textbox « » Textfile « » CSV «
286602 283042 276687 277915 277128 277103 275037 270150 267140 264993 287259 291186 292300 288186 281477 282656 280190 280408 276836 275216 274352 271311 289802 290726 292300 278506 269826 265861 269034 264176 255198 253353 246057 235372 258556 260993 254663 250643 243422 247105 248541 245039 237080 237085 225554 226839 247934 248333 246969 245098 246263 255765 264319 268347 273046 273963 267430 271993 292710 295881 294563
 
Output produced by software:


Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'George Udny Yule' @ 72.249.76.132


Classical Decomposition by Moving Averages
tObservationsFitTrendSeasonalRandom
1286602NANA9297.1814236111NA
2283042NANA3328.45225694445NA
3276687NANA-2075.53732638890NA
4277915NANA448.275173611125NA
5277128NANA2992.82725694443NA
6277103NANA1858.63975694443NA
7275037275924.577256944278090.916666667-2166.33940972222-887.577256944438
8270150274606.983506944278542.666666667-3935.68315972222-4456.9835069445
9267140269579.108506944278956.583333333-9377.4748263889-2439.10850694444
10264993266656.931423611279353.708333333-12696.7769097222-1663.93142361112
11287259282483.962673611279678.8333333332805.129340277794775.03732638888
12291186289465.431423611279944.1259521.30642361111720.56857638893
13292300289453.973090278280156.7916666679297.18142361112846.02690972225
14288186283771.285590278280442.8333333333328.452256944454414.71440972225
15281477278878.879340278280954.416666667-2075.537326388902598.12065972225
16282656281966.441840278281518.166666667448.275173611125689.558159722248
17280190284880.202256944281887.3752992.82725694443-4690.20225694444
18280408283832.806423611281974.1666666671858.63975694443-3424.80642361107
19276836279788.660590278281955-2166.33940972222-2952.66059027775
20275216277615.983506944281551.666666667-3935.68315972222-2399.98350694444
21274352271285.400173611280662.875-9377.47482638893066.59982638893
22271311266780.848090278279477.625-12696.77690972224530.15190972225
23289802281118.1293402782783132805.129340277798683.87065972225
24290726286693.139756944277171.8333333339521.30642361114032.86024305556
25292300284891.098090278275593.9166666679297.18142361117408.90190972225
26278506277109.827256944273781.3753328.452256944451396.17274305562
27269826269615.921006944271691.458333333-2075.53732638890210.078993055562
28265861269463.316840278269015.041666667448.275173611125-3602.31684027775
29269034269208.493923611266215.6666666672992.82725694443-174.493923611124
30264176265533.514756944263674.8751858.63975694443-1357.51475694444
31255198258701.452256944260867.791666667-2166.33940972222-3503.45225694444
32253353254202.941840278258138.625-3935.68315972222-849.941840277752
33246057246500.025173611255877.5-9377.4748263889-443.025173611066
34235372241299.056423611253995.833333333-12696.7769097222-5927.0564236111
35258556255165.587673611252360.4583333332805.129340277793390.41232638891
36260993260230.514756944250709.2083333339521.3064236111762.485243055562
37254663258454.098090278249156.9166666679297.1814236111-3791.09809027778
38250643251052.618923611247724.1666666673328.45225694445-409.618923611124
39243422244116.504340278246192.041666667-2075.53732638890-694.504340277752
40247105245430.483506944244982.208333333448.2751736111251674.51649305559
41248541247176.910590278244184.0833333332992.827256944431364.08940972228
42245039245072.6397569442432141858.63975694443-33.6397569444089
43237080240199.577256944242365.916666667-2166.33940972222-3119.57725694444
44237085237878.608506944241814.291666667-3935.68315972222-793.608506944438
45225554232324.150173611241701.625-9377.4748263889-6770.15017361109
46226839229484.056423611242180.833333333-12696.7769097222-2645.05642361109
47247934246004.212673611243199.0833333332805.129340277791929.78732638893
48248333254348.973090278244827.6666666679521.3064236111-6015.97309027775
49246969256594.598090278247297.4166666679297.1814236111-9625.59809027778
50245098253661.035590278250332.5833333333328.45225694445-8563.03559027775
51246263251538.462673611253614-2075.53732638890-5275.4626736111
52255765257688.525173611257240.25448.275173611125-1923.52517361109
53264319263980.160590278260987.3333333332992.82725694443338.839409722248
54268347266692.806423611264834.1666666671858.639756944431654.19357638888
55273046266632.077256944268798.416666667-2166.339409722226413.92274305556
56273963NANA-3935.68315972222NA
57267430NANA-9377.4748263889NA
58271993NANA-12696.7769097222NA
59292710NANA2805.12934027779NA
60295881NANA9521.3064236111NA
61294563NANANANA
 
Charts produced by software:
http://www.freestatistics.org/blog/date/2010/Dec/09/t12919236715k1s6ojk8i1m4x3/13e0j1291923799.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t12919236715k1s6ojk8i1m4x3/13e0j1291923799.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/09/t12919236715k1s6ojk8i1m4x3/23e0j1291923799.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t12919236715k1s6ojk8i1m4x3/23e0j1291923799.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/09/t12919236715k1s6ojk8i1m4x3/3wnz41291923799.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t12919236715k1s6ojk8i1m4x3/3wnz41291923799.ps (open in new window)


http://www.freestatistics.org/blog/date/2010/Dec/09/t12919236715k1s6ojk8i1m4x3/4oxh71291923799.png (open in new window)
http://www.freestatistics.org/blog/date/2010/Dec/09/t12919236715k1s6ojk8i1m4x3/4oxh71291923799.ps (open in new window)


 
Parameters (Session):
par1 = additive ; par2 = 12 ;
 
Parameters (R input):
par1 = additive ; par2 = 12 ;
 
R code (references can be found in the software module):
par2 <- as.numeric(par2)
x <- ts(x,freq=par2)
m <- decompose(x,type=par1)
m$figure
bitmap(file='test1.png')
plot(m)
dev.off()
mylagmax <- length(x)/2
bitmap(file='test2.png')
op <- par(mfrow = c(2,2))
acf(as.numeric(x),lag.max = mylagmax,main='Observed')
acf(as.numeric(m$trend),na.action=na.pass,lag.max = mylagmax,main='Trend')
acf(as.numeric(m$seasonal),na.action=na.pass,lag.max = mylagmax,main='Seasonal')
acf(as.numeric(m$random),na.action=na.pass,lag.max = mylagmax,main='Random')
par(op)
dev.off()
bitmap(file='test3.png')
op <- par(mfrow = c(2,2))
spectrum(as.numeric(x),main='Observed')
spectrum(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
spectrum(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
spectrum(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
bitmap(file='test4.png')
op <- par(mfrow = c(2,2))
cpgram(as.numeric(x),main='Observed')
cpgram(as.numeric(m$trend[!is.na(m$trend)]),main='Trend')
cpgram(as.numeric(m$seasonal[!is.na(m$seasonal)]),main='Seasonal')
cpgram(as.numeric(m$random[!is.na(m$random)]),main='Random')
par(op)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Classical Decomposition by Moving Averages',6,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'t',header=TRUE)
a<-table.element(a,'Observations',header=TRUE)
a<-table.element(a,'Fit',header=TRUE)
a<-table.element(a,'Trend',header=TRUE)
a<-table.element(a,'Seasonal',header=TRUE)
a<-table.element(a,'Random',header=TRUE)
a<-table.row.end(a)
for (i in 1:length(m$trend)) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
a<-table.element(a,x[i])
if (par1 == 'additive') a<-table.element(a,m$trend[i]+m$seasonal[i]) else a<-table.element(a,m$trend[i]*m$seasonal[i])
a<-table.element(a,m$trend[i])
a<-table.element(a,m$seasonal[i])
a<-table.element(a,m$random[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
 





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Software written by Ed van Stee & Patrick Wessa


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